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Recent research shows a growing interest in adopting touch interaction for robot learning, yet it remains challenging to efficiently acquire high-quality, structured tactile data at a low cost. In this study, we propose the design of vision-based soft robotic tongs to generate reproducible and shareable data of tactile interaction for learning. These are all components in a foldable fanny pack,including monocular camera, camera pedestal,pad with makers and meta-fingers and tongs. We further developed a web-based platform for convenient data collection and a portable assembly that can be deployed within minutes. We trained a simple network to infer the 6D force and torque using relative pose data from markers on the fingers and reached a reasonably high accuracy but cost only 50 dollars per set. The recorded tactile data is downloadable for robot learning. We further demonstrated the system for interacting with robotic arms in manipulation learning and remote control. We have open-sourced the whole system on GitHub with further information. We used a soft, 3D meta-finger structure as the physical interface for interacting with the objects, which can be conveniently fixed either at the tip of the tongs or the gripper. The soft structure takes a unique design as a meta-material capable of generating passive geometric adaptation to object contact in any direction. In this way, we can ensure a transferable interaction between the tongs, or the gripper, with the objects. The soft meta-finger passively deforms to the object geometry, which enhances its performance in object grasping while providing a visible distortion that can be captured by a camera. The recorded data is recorded locally in the browser but not in the server. The network used in the training and the results are described in the paper. To make the system comparable with the standard parallel two-finger grippers used in the industry, we leveraged the existing design of typical kitchen tongs to systematically reduce the dimensionality of multi-fingered hand motion into two-fingered tongs. By comparing the work of double finger gripper and human hand, it can be found that most tasks can be completed by human hand for two-finger gripper is not difficult. In addition, we have made a number of examples to demonstrate the remote manipulation of a robotic arm using soft fingers. Subsequently, we also made demonstration of applying the collected data for Gaussian Learning. We also conducted a pilot program using the proposed system for teaching a robot learning course at University level during the spring semester of 2022. The teaching team prepared the proposed design so that it can be fabricated at a low-cost, accessed with ease of engineering, and also provide a relatively rich data so that students can use for training models of their own.